JupyterLab: Sane Defaults and Extensions for Basic DS Work
08 Nov 2021 Minimum Viable Jupyterlab configuration for reasonably pleasant DS work in Python
Read More ›Git Examples: Searching the Git History
01 Nov 2021 Examples on how to search the git logs and history for modified files, modified code, how to retrieve past files from the history, etc.
Bash Options with Set: Examples and reference
31 Oct 2021 Examples and reference for most commonly-used bash options you can set/unset with the set builtin.
Read More ›paper-summary natural-language-processing
Paper Summary: Exploring the Limits of Transfer Learning with a Unified Text-to-text Transformer
29 Aug 2021 Summary of the 2020 article "Exploring the Limits of Transfer Learning with a Unified Text-to-text Transformer" by Raffel et al. AKA the T5 article.
Python 3 Examples: Creating, Deleting, Updating directories
08 Aug 2021 Lots of examples on how to create, remove, change and otherwise interact with directories with Python 3+ (Assuming unix-like Operating Systems such as Linux and MacOS)
Read More ›Measuring Function Running Times in Python
08 Jul 2021 Really simple code to help profile individual method calls to find bottlenecks in Python code.
Read More ›Matplotlib examples: Number Formatting for Axis Labels
29 Jun 2021 Some examples on how to properly format axis labels, add thousands separator, format axis labels to make them easier to read, etc.
Read More ›Matplotlib Examples: Plots with String Axis Labels
28 Jun 2021 Examples on how to create plots where one of the axis is not a list of integers/floats but a list of strings.
Read More ›paper-summary machine-learning-engineering machine-learning
Paper Summary: Identifying Mislabeled Instances in Classification Datasets
28 Jun 2021 Summary of the 2019 article "Identifying Mislabeled Instances in Classification Datasets" by Mueller and Markert.
scalability software-architecture
Probabilistic Behaviour can Sometimes Replace State at Scale
28 Jun 2021 You can sometimes forgo keeping state for an application if you are operating at scale and you can afford some innacuracies.
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